Addressing Opioid-Associated Constipation Using Quality Oncology Practice Initiative Scores and Plan-Do-Study-Act Cycles
Bibliographic record
Abstract
Using the Quality Oncology Practice Initiative, an affiliate program of ASCO, we outlined opioid-associated constipation (OAC) as a subject in need of quality improvement (QI) in our fellowship program at the University of Arkansas for Medical Sciences and Central Arkansas Veterans Healthcare System. We initiated a fellow-led QI project to advance the quality of patient care and provide a valuable avenue for QI training of young physicians. Fellows organized meetings with all stakeholders, addressed the scope of the problem, and devised strategies for OAC management. Monthly meetings were organized using Plan-Do-Study-Act principles. Mandatory check boxes were inserted into our electronic medical record templates to remind all physicians to identify patients on opioid medications and assess and address OAC. Final chart audit and patient satisfaction surveys were performed 6 months after project initiation. Assessment of OAC improved from 52% at baseline to 92% ( P < .003). This improvement corresponded with high patient satisfaction scores, with 90% of surveyed patients reporting adequate management of their constipation. In this QI initiative, we showed that participation in ASCO's Quality Oncology Practice Initiative helps identify areas in need of QI, and such fellow-led QI projects can serve as models for QI training of young physicians.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".